Managing the performance of an electronic device

• US 8,560,687 B1
• Filed: 10/18/2011
• Issued: 10/15/2013
• Est. Priority Date: 03/30/2007
• Status: Active Grant

• Current Assignee: United Services Automobile Association
• Original Assignee: United Services Automobile Association
• Inventors: Becker, Glen Alan
• Primary Examiner(s): Wang, Liangche A
• Assistant Examiner(s): LIN, SHERMAN L
• Application Number:
  US13/276,095
• Time in Patent Office: 728 Days
• Field of Search: 700 28- 55, 702179-186, 706 1- 62, 706/908, 706/909, 706/916, 706/917, 706/934, 708270-277, 709/223, 709/224
• US Class Current: 709/224
• CPC Class Codes: G06F 11/3442 for planning or managing the needed capacity
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 11/3495 for systems
• CPC Class Codes: G06F 2201/81 Threshold
• CPC Class Codes: H04L 43/04 Processing captured monitoring data, e.g. for logfile generation

Abstract

A performance management system and method for generating a plurality of forecasts for one or more electronic devices is presented. The forecasts are generated from stored performance data and analyzed to determine which devices are likely to experience performance degradation within a predetermined period of time. A single forecast is extracted for further analysis such that computer modeling may be performed upon the performance data to enable the user to predict when device performance will begin to degrade. In one embodiment, graphical displays are created for those devices forecasted to perform at an undesirable level such that suspect devices may be subjected to further analysis.

Claims

1. A method comprising:

using one or more processors to perform the following;

collecting and analyzing historic resource utilization data relating to a plurality of computing devices, the historic resource utilization data representing a load placed on a set of finite resources over a predefined period of time, wherein irrelevant data from the historic resource utilization data is identified and eliminated;

generating an exponential growth model for each computing device based on analysis of the historic resource utilization data;

generating a linear growth model for a particular computing device by adjusting the exponential growth model into the linear growth model when a defined criterion which pertains to the exponential growth model is met, wherein the defined criterion includes when the historic resource utilization data generates a slope of the exponential growth model that exceeds a threshold scale factor of a slope of an end of a most recent portion of the historic resource utilization data in the exponential growth model;

creating a resource utilization forecast for each computing device from the exponential growth model or the linear growth model;

receiving an assignment of a threshold value for the resource utilization forecast of each computing device;

identifying an earliest forecasted date the threshold value is exceeded for the particular computing device based on the resource utilization forecast and the threshold value of each computing device; and

sorting a number of resource utilization forecasts by the identified date for each of the number of resource utilization forecasts, wherein the number of resource utilization forecasts includes linear and exponential growth models.

2. The method of claim 1, wherein the threshold value for the linear growth model is substantially at an 84% confidence interval.

3. The method of claim 1, wherein the threshold value for the linear growth model creates a 1-sigma safety factor.

4. The method of claim 1, further comprising determining a type of the computing device based on a format of the collected historic resource utilization data.

5. The method of claim 1, wherein identifying an earliest forecasted date comprises identifying where the threshold v/due is exceeded first for the linear growth model and the exponential growth model.

6. The method of claim 1, further comprising:

determining whether the particular computing device is in need of attention to prevent a failure or performance degradation based on the resource utilization forecast for the particular computing device and the assignment of the threshold value; and

adjusting workload or capability of the plurality of computing devices based on a determination that the particular computing device is in need of attention.

7. A non-transitory computer-readable medium having computer-readable instructions stored thereon that are executable by a processor to:

collect and analyze historic resource utilization data relating to a plurality of computing devices, the historic resource utilization data representing a load placed on a set of finite resources over a predefined period of time wherein irrelevant data from the historic resource utilization data is identified and eliminated;

generate an exponential growth model for each computing device based on analysis of the historic resource utilization data;

generate a linear growth model for a particular computing device by adjusting the exponential growth model into the linear growth model when a defined criterion which pertains to the exponential growth model is met, wherein the defined criterion includes when the historic resource utilization data generates a slope of the exponential growth model that exceeds a multiple of a slope of an end of a most recent portion of the historic resource utilization data in the exponential growth model;

create a resource utilization forecast for each computing device from the exponential growth model or the linear growth model;

receive an assignment of a threshold value for the resource utilization forecast of each computing device;

identify an earliest forecasted date the threshold value is exceeded for the particular computing device based on the resource utilization forecast and the threshold value of each computing device; and

sort a number of resource utilization forecasts by the identified date for each of the number of resource utilization forecasts, wherein the number of resource utilization forecasts includes linear and exponential growth models.

8. The computer readable medium of claim 7, wherein the threshold value for the linear growth model is substantially at an 84% confidence interval.

9. The computer readable medium of claim 7, wherein the threshold value for the linear growth model creates a 1-sigma safety factor.

10. The computing computer readable medium of claim 7, further comprising instructions stored thereon that are executed by the processor to determine a type of the computing device based on a format of the collected historic resource utilization data.

11. The computer readable medium of claim 7, wherein the instructions to identify the earliest forecasted date cause the processor to identify where the threshold value is exceeded first for the linear growth model and the exponential growth model.

12. The computer readable medium of claim 7, further comprising instructions stored thereon that are executed by the processor to:

determine whether the particular computing device is in need of attention to prevent a failure or performance degradation based on the resource utilization forecast for the particular computing device and the assignment of the threshold value; and

adjust workload or capability of the plurality of computing devices based on a determination that the particular computing device is in need of attention.

13. A system comprising:

a processor and memory;

a subsystem deployed in the memory and executed by the processor to collect and analyze historic resource utilization data relating to a plurality of computing devices, the historic resource utilization data representing a load placed on a set of finite resources over a predefined period of time, wherein irrelevant data from the historic resource utilization data is identified and eliminated;

a subsystem deployed in the memory and executed by the processor to generate an exponential growth model for each computing device based on analysis of the historic resource utilization data;

a subsystem deployed in the memory and executed by the processor to generate a linear growth model for a particular computing device by adjusting the exponential growth model into the linear growth model when a defined criterion which pertains to the exponential growth model is met, wherein the defined criterion includes when the historic resource utilization data generates a slope of the exponential growth model that exceeds a multiple of a slope of an end of a most recent portion of the historic resource utilization data in the exponential growth model;

a subsystem deployed in the memory and executed by the processor to create a resource utilization forecast for each computing device from the exponential growth model or the linear growth model;

a subsystem deployed in the memory and executed by the processor receive an assignment of a threshold value for the resource utilization forecast of each computing device;

a subsystem deployed in the memory and executed by the processor identify an earliest forecasted date the threshold value is exceeded for the particular computing device based on the resource utilization forecast and the threshold value of each computing device; and

a subsystem deployed in the memory and executed by the processor to sort a number of resource utilization forecasts by the identified date for each of the number of resource utilization forecasts, wherein the number of resource utilization forecasts includes linear and exponential growth models.

14. The system of claim 13, wherein the threshold value for the linear growth model is substantially at an 84% confidence interval.

15. The system of claim 13, wherein the threshold value for the linear growth model creates a 1-sigma safety factor.

16. The system of claim 13, wherein identification of the earliest forecasted date includes identification of where the threshold value is exceeded first for the linear growth model and the adjusted exponential growth model.

17. The system of claim 13, further comprising:

determining whether the particular computing device is in need of attention to prevent a failure or performance degradation based on the resource utilization forecast for the particular computing device and the assignment of the threshold value; and

adjusting workload or capability of the plurality of computing devices based on a determination that the particular computing device is in need of attention.